Ultrasonic Logging Image Denoising Based on CNN and Feature Attention

Abstract

Various kinds of noise will be produced during the process of ultrasonic logging in high temperature and high-pressure environment under oil wells, which is blurring the logging image. This paper presents a novel end-to-end denoising model (ULNet) based on CNN and feature attention to address this problem and remove the noise from ultrasonic logging images. Our method mainly includes feature attention, feature enhancement based on residual model and reconstruction for ultrasonic logging image. Feature enhancement based on a residual model integrates global and local features to increase the expressive ability of the denoising model. Feature attention is used to distinguish the channel feature weights, and effective for blind denoising of actual images. Kernel dilation and skip-connection is used to reduce the computational cost during training. The Noise mapping results are used to reconstruct a clean image. Comprehensive quantitative and qualitative evaluations of results for selected study datasets collected at six oil wells in China show that this model is a feasible and effective means for denoising ultrasonic logging images. Overall, ULNet shows potential for practical ultrasonic logging images denoising.INDEX TERMS Ultrasonic logging image denoising, CNN, feature attention, residual learning. I. INTRODUCTION

Synthetic aperture radar images denoising based on multi-scale attention cascade convolutional neural network

“…Moreover, comparing different algorithm models can help us understand their performance on various datasets and evaluation metrics. In this study, we evaluate the performance of MSAC-Net by comparing it with state-of-the-art algorithm models, including WNNM [14], SAR-BM3D [8], SAR-CNN [23], GAN [21], SAR2SAR [25], and AGSDNet [28]. To ensure a fair comparison, we utilize the default settings provided by the authors in their respective algorithm literature and calculate PSNR and SSIM as error metrics to comprehensively evaluate their practical application value.…”

“…Rudin et al [11] proposed a total variation (TV) model for denoising, which achieved good results in speckle noise removal despite the staircasing effect it produced. In recent years, literature [12][13][14] has made significant improvements and optimizations to the TV denoising algorithm. By minimizing the staircasing effect while preserving image edge information, the improved algorithm model maintains its adaptability.…”